Statistical and Topological Quantification of Shape Features in Biomedical Imaging

本项目通过结合统计和拓扑数据分析技术，开发了医学图像分析的新方法。总体目标是鲁棒地检测生物医学图像中的形态特征，通过无标记的“指纹”振动光谱方法获得，并根据其疾病状态对图像进行分类。这种先进的光谱学和新型成像方法能够提取生物系统（包括活细胞）的化学成分，达到前所未有的细节水平，因此可用于准确的医学诊断和疾病的早期检测。成像技术给出了多组分光谱信息，其具有化学和结构信息，这些信息可能很复杂，难以以稳健的非主观方式解开。然而，目前，诊断仅取决于临床医生的专业知识，而机器学习数据驱动的分类器通常受到少量训练样本的限制。该项目将开发新的统计模型，使用拓扑摘要来创建强大的图像分类方法，而无需大量的训练集。

This project develops new methods for medical image analysis by combining techniques from statistical and topological data analysis. The overall objective is to robustly detect morphological features in biomedical images obtained by label-free 'finger-printing' vibrational spectroscopy methods and classify the images according to their disease status. Such advanced spectroscopy and novel imaging methods are able to extract the chemical composition of biological systems, including living cells, to an unprecedented level of detail, and thus can be used for accurate medical diagnosis and early detection of diseases. The imaging techniques give multicomponent spectral information bearing chemical and structural information that can be complex to disentangle in a robust non-subjective manner. However, at the moment, the diagnosis depends solely on the clinician's expertise, while machine learning data-driven classifiers are limited by the typically small number of training samples. This project will develop novel statistical models which use topological summaries to create powerful methods for image classification without the need of vast training sets.